METHODS, SYSTEMS AND APPARATUSES FOR DETECTING SEIZURE AND NON-SEIZURE STATES

• US 20140031635A1
• Filed: 07/25/2012
• Published: 01/30/2014
• Est. Priority Date: 04/23/2012
• Status: Active Grant

• Current Assignee: LivaNova USA, Inc. (LivaNova Plc)
• Original Assignee: Cyberonics, Inc
• Inventors: SABESAN, Shivkumar
• Granted Patent: US 9,681,836 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/33 specially adapted for cooperation with other devices
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence

Abstract

Methods, systems, and apparatuses for detecting seizure events are disclosed, having at least one accelerometer to be positioned on a patient and configured to collect acceleration data and a processor in communication with the at least one accelerometer and configured to receive acceleration data from the at least one accelerometer. The processor may apply at least one non-linear operator to the acceleration data to determine whether the acceleration data indicates an event, such application including calculation of a non-linear energy of the acceleration data and performance of at least one secondary analysis to determine whether the event is a seizure.

Claims

1. A system comprising:

at least one accelerometer for positioning on a patient, the at least one accelerometer configured to collect acceleration data; and

a processor in communication with the at least one accelerometer, the processor configured to receive acceleration data from the at least one accelerometer, wherein the processor is configured to apply one or more non-linear operators to the acceleration data to determine whether the acceleration data indicates an event, wherein application of the non-linear operator to the acceleration data includes calculation of a non-linear energy of the acceleration data and performance of at least one secondary analysis to determine whether the event is a seizure.

2. The system of claim 1, further comprising:

a database configured to maintain a record of one or more of the events.

3. The system of claim 1, wherein the at least one accelerometer includes a first accelerometer and a second accelerometer, wherein the first accelerometer and the second accelerometer are positioned at different locations on the patient.

4. The system of claim 1, wherein the at least one secondary analysis includes a dynamic activity analysis of the acceleration data, wherein the dynamic activity of the acceleration data is determined and compared to a first predetermined threshold.

5. The system of claim 1, wherein the at least one secondary analysis includes an analysis of frequency domain features of the acceleration data, wherein the frequency domain features of the acceleration data are determined and compared to a second predetermined threshold.

6. The system of claim 1, wherein at least one of the non-linear operators applied to the acceleration data by the processor is a Lie Bracket operator.

7. The system of claim 6, wherein the acceleration data provided to the processor includes a first acceleration signal having an x component, a second acceleration signal having y component, and a third acceleration component having z component.

8. The system of claim 7, wherein the non-linear energy is determined by calculating a non-linear energy for each component of the first, second, and third acceleration signals to provide three non-linear energy results.

9. The system of claim 8, wherein the three non-linear energy results are averaged together to calculate an average non-linear energy of the acceleration data.

10. The system of claim 1, further comprising:

one or more physiological monitoring systems for collecting one or more physiological measurements of the patient, the one or more physiological measurements sent to the processor, and the processor configured to analyze the one or more physiological measurements to determine whether the one or more physiological measurements support a determination that an event is a seizure.

11. The system of claim 10, wherein the one or more physiological monitoring systems includes an EKG monitoring system configured to collect EKG measurements from the patient.

12. The system of claim 11, wherein the analysis of EKG measurements to determine whether the EKG measurements support a determination that the event is a seizure include use of a relative heart rate detection algorithm.

13. The system of claim 12, wherein a further analysis of the EKG measurements is performed, which includes a noise analysis, and wherein the EKG measurements are disregarded in determining whether the event is a seizure when the noise analysis determines that level of noise in the EKG measurements is too high.

14. The system of claim 10, wherein the one or more physiological monitoring systems includes one or more of an EKG monitoring system, a respiration monitoring system, and a skin resistivity monitoring system.

15. An apparatus comprising:

at least one accelerometer for positioning on a patient, the at least one accelerometer configured to collect acceleration data; and

a processor in communication with the at least one accelerometer, the processor configured to receive acceleration data from the at least one accelerometer, wherein the processor is configured to apply a non-linear operator to the acceleration data to determine whether the acceleration data indicates an event, wherein application of the non-linear operator to the acceleration data includes calculation of a non-linear energy of the acceleration data and performance of at least one secondary analysis to determine whether the event is a seizure.

16. The apparatus of claim 15, wherein the non-linear operator applied to the acceleration data by the processor is a Lie Bracket operator.

17. A method, comprising:

receiving acceleration data at a processor from at least one accelerometer positioned on the patient; and

applying a non-linear operator to the acceleration data at the processor to determine whether the acceleration data indicates an event, wherein applying the non-linear operator to the acceleration data includes calculating a non-linear energy of the acceleration data.

18. The method of claim 17, further comprising:

performing a secondary analysis on the acceleration data at the processor to determine whether the event is a seizure.

19. The method of claim 18, further comprising:

maintaining a record in a database of one or more of the events.

20. The method of claim 18, wherein the secondary analysis of the acceleration data includes a dynamic activity analysis of the acceleration data, wherein the dynamic activity of the acceleration data is determined and compared to a first predetermined threshold.

21. The method of claim 18, wherein the secondary analysis of the acceleration data includes an analysis of frequency domain features of the acceleration data, wherein the frequency domain features of the acceleration data are determined and compared to a first predetermined threshold.

22. The method of claim 17, wherein the non-linear operator applied to the acceleration data by the processor is a Lie Bracket operator.

23. The method of claim 22, wherein application of the Lie Bracket operator to the acceleration data includes use of a second order Lie Bracket equation.

24. The method of claim 22, wherein the acceleration data received at the processor includes a first acceleration signal having an x component, a second acceleration signal having y component, and a third acceleration component having z component.

25. The method of claim 24, wherein calculating the non-linear energy further comprises calculating a non-linear energy for each component of the first, second, and third acceleration signals to provide three non-linear energy results.

26. The method of claim 25, wherein calculating the non-linear energy further comprises averaging the three non-linear energy results to provide an average non-linear energy.